A multispecies <scp>1D</scp> concentration distribution model for coarse‐particle slurries
Bibliographic record
Abstract
Abstract Coarse‐particle (settling) slurry pipelines are key process units in many industries. In Canada's oil sands operations, such pipelines represent hundreds of millions of dollars of infrastructure investment and transport thousands of tonnes of solids every hour. The ability to determine key design/operating parameters of a settling slurry, such as frictional pressure gradient and deposition velocity, is dependent on the accuracy of the concentration distribution model used to determine the local concentration of coarse solids as a function of vertical position within the pipe. Accurate predictions of the concentration profile are also required for physics‐based models of particle‐impact erosion in slurry pipelines. In this study, an improved concentration distribution model was developed. The classic Schmidt‐Rouse turbulent diffusion equation forms the basis for the model. The improvements made here include the use of a more suitable, high‐concentration hindered settling velocity correlation, and a simple semi‐empirical correlation for the particle diffusivity, which is shown to be dependent only upon the terminal particle settling velocity and the Kolmogorov turbulent velocity scale. The model is applicable to slurries with broad size distributions and/or species with different densities. The performance of the model is tested against numerous slurry flow conditions, including particles from 70 μm to 8 mm in diameter, pipe diameters of 76 mm ≤ D ≤ 500 mm, and in situ solids volume concentrations from 0.10 to 0.45. The concentration distribution predictions are shown to be in excellent agreement with the measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".